[GH-PAGES] Updated website
This commit is contained in:
Philippe Tillet
@@ -1,4 +1,4 @@
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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: 456fb2bf2c82c803cfdaa7bcb3e778a9
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config: aa051e1ca67e6f2658629dbe2c30cba0
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tags: 645f666f9bcd5a90fca523b33c5a78b7
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@@ -24,9 +24,11 @@ def add_kernel(
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y_ptr, # *Pointer* to second input vector
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output_ptr, # *Pointer* to output vector
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n_elements, # Size of the vector
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time_start_ptr, time_end_ptr,
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BLOCK_SIZE: tl.constexpr, # Number of elements each program should process
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# NOTE: `constexpr` so it can be used as a shape value
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):
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tl.atomic_min(time_start_ptr, tl.clock())
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# There are multiple 'program's processing different data. We identify which program
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# we are here
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pid = tl.program_id(axis=0) # We use a 1D launch grid so axis is 0
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@@ -45,6 +47,7 @@ def add_kernel(
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output = x + y
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# Write x + y back to DRAM
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tl.store(output_ptr + offsets, output, mask=mask)
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tl.atomic_max(time_end_ptr, tl.clock())
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# %%
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@@ -53,6 +56,8 @@ def add_kernel(
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def add(x: torch.Tensor, y: torch.Tensor):
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time_start = torch.zeros(1, dtype=torch.int64, device='cuda')
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time_end = torch.zeros(1, dtype=torch.int64, device='cuda')
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# We need to preallocate the output
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output = torch.empty_like(x)
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assert x.is_cuda and y.is_cuda and output.is_cuda
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@@ -65,7 +70,7 @@ def add(x: torch.Tensor, y: torch.Tensor):
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# - each torch.tensor object is implicitly converted into a pointer to its first element.
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# - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel
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# - don't forget to pass meta-parameters as keywords arguments
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add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
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add_kernel[grid](x, y, output, n_elements, time_start, time_end, BLOCK_SIZE=1024)
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# We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
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# running asynchronously at this point.
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return output
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@@ -33,7 +33,7 @@
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},
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"outputs": [],
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"source": [
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"import torch\n\nimport triton\nimport triton.language as tl\n\n\n@triton.jit\ndef add_kernel(\n x_ptr, # *Pointer* to first input vector\n y_ptr, # *Pointer* to second input vector\n output_ptr, # *Pointer* to output vector\n n_elements, # Size of the vector\n BLOCK_SIZE: tl.constexpr, # Number of elements each program should process\n # NOTE: `constexpr` so it can be used as a shape value\n):\n # There are multiple 'program's processing different data. We identify which program\n # we are here\n pid = tl.program_id(axis=0) # We use a 1D launch grid so axis is 0\n # This program will process inputs that are offset from the initial data.\n # for instance, if you had a vector of length 256 and block_size of 64, the programs\n # would each access the elements [0:64, 64:128, 128:192, 192:256].\n # Note that offsets is a list of pointers\n block_start = pid * BLOCK_SIZE\n offsets = block_start + tl.arange(0, BLOCK_SIZE)\n # Create a mask to guard memory operations against out-of-bounds accesses\n mask = offsets < n_elements\n # Load x and y from DRAM, masking out any extra elements in case the input is not a\n # multiple of the block size\n x = tl.load(x_ptr + offsets, mask=mask)\n y = tl.load(y_ptr + offsets, mask=mask)\n output = x + y\n # Write x + y back to DRAM\n tl.store(output_ptr + offsets, output, mask=mask)"
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"import torch\n\nimport triton\nimport triton.language as tl\n\n\n@triton.jit\ndef add_kernel(\n x_ptr, # *Pointer* to first input vector\n y_ptr, # *Pointer* to second input vector\n output_ptr, # *Pointer* to output vector\n n_elements, # Size of the vector\n time_start_ptr, time_end_ptr,\n BLOCK_SIZE: tl.constexpr, # Number of elements each program should process\n # NOTE: `constexpr` so it can be used as a shape value\n):\n tl.atomic_min(time_start_ptr, tl.clock())\n # There are multiple 'program's processing different data. We identify which program\n # we are here\n pid = tl.program_id(axis=0) # We use a 1D launch grid so axis is 0\n # This program will process inputs that are offset from the initial data.\n # for instance, if you had a vector of length 256 and block_size of 64, the programs\n # would each access the elements [0:64, 64:128, 128:192, 192:256].\n # Note that offsets is a list of pointers\n block_start = pid * BLOCK_SIZE\n offsets = block_start + tl.arange(0, BLOCK_SIZE)\n # Create a mask to guard memory operations against out-of-bounds accesses\n mask = offsets < n_elements\n # Load x and y from DRAM, masking out any extra elements in case the input is not a\n # multiple of the block size\n x = tl.load(x_ptr + offsets, mask=mask)\n y = tl.load(y_ptr + offsets, mask=mask)\n output = x + y\n # Write x + y back to DRAM\n tl.store(output_ptr + offsets, output, mask=mask)\n tl.atomic_max(time_end_ptr, tl.clock())"
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]
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},
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{
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@@ -51,7 +51,7 @@
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},
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"outputs": [],
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"source": [
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"def add(x: torch.Tensor, y: torch.Tensor):\n # We need to preallocate the output\n output = torch.empty_like(x)\n assert x.is_cuda and y.is_cuda and output.is_cuda\n n_elements = output.numel()\n # The SPMD launch grid denotes the number of kernel instances that run in parallel.\n # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]\n # In this case, we use a 1D grid where the size is the number of blocks\n grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n # NOTE:\n # - each torch.tensor object is implicitly converted into a pointer to its first element.\n # - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel\n # - don't forget to pass meta-parameters as keywords arguments\n add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)\n # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still\n # running asynchronously at this point.\n return output"
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"def add(x: torch.Tensor, y: torch.Tensor):\n time_start = torch.zeros(1, dtype=torch.int64, device='cuda')\n time_end = torch.zeros(1, dtype=torch.int64, device='cuda')\n # We need to preallocate the output\n output = torch.empty_like(x)\n assert x.is_cuda and y.is_cuda and output.is_cuda\n n_elements = output.numel()\n # The SPMD launch grid denotes the number of kernel instances that run in parallel.\n # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]\n # In this case, we use a 1D grid where the size is the number of blocks\n grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)\n # NOTE:\n # - each torch.tensor object is implicitly converted into a pointer to its first element.\n # - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel\n # - don't forget to pass meta-parameters as keywords arguments\n add_kernel[grid](x, y, output, n_elements, time_start, time_end, BLOCK_SIZE=1024)\n # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still\n # running asynchronously at this point.\n return output"
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]
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},
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{
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@@ -31,7 +31,7 @@ In this tutorial, you will write a simple vector addition using Triton and learn
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Compute Kernel
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--------------------------
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.. GENERATED FROM PYTHON SOURCE LINES 14-50
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.. GENERATED FROM PYTHON SOURCE LINES 14-53
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.. code-block:: default
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@@ -48,9 +48,11 @@ Compute Kernel
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y_ptr, # *Pointer* to second input vector
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output_ptr, # *Pointer* to output vector
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n_elements, # Size of the vector
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time_start_ptr, time_end_ptr,
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BLOCK_SIZE: tl.constexpr, # Number of elements each program should process
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# NOTE: `constexpr` so it can be used as a shape value
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):
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tl.atomic_min(time_start_ptr, tl.clock())
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# There are multiple 'program's processing different data. We identify which program
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# we are here
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pid = tl.program_id(axis=0) # We use a 1D launch grid so axis is 0
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@@ -69,6 +71,7 @@ Compute Kernel
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output = x + y
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# Write x + y back to DRAM
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tl.store(output_ptr + offsets, output, mask=mask)
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tl.atomic_max(time_end_ptr, tl.clock())
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@@ -78,18 +81,20 @@ Compute Kernel
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.. GENERATED FROM PYTHON SOURCE LINES 51-53
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.. GENERATED FROM PYTHON SOURCE LINES 54-56
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Let's also declare a helper function to (1) allocate the `z` tensor
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and (2) enqueue the above kernel with appropriate grid/block sizes.
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.. GENERATED FROM PYTHON SOURCE LINES 53-74
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.. GENERATED FROM PYTHON SOURCE LINES 56-79
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.. code-block:: default
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def add(x: torch.Tensor, y: torch.Tensor):
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time_start = torch.zeros(1, dtype=torch.int64, device='cuda')
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time_end = torch.zeros(1, dtype=torch.int64, device='cuda')
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# We need to preallocate the output
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output = torch.empty_like(x)
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assert x.is_cuda and y.is_cuda and output.is_cuda
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@@ -102,7 +107,7 @@ and (2) enqueue the above kernel with appropriate grid/block sizes.
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# - each torch.tensor object is implicitly converted into a pointer to its first element.
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# - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel
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# - don't forget to pass meta-parameters as keywords arguments
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add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
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add_kernel[grid](x, y, output, n_elements, time_start, time_end, BLOCK_SIZE=1024)
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# We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
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# running asynchronously at this point.
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return output
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@@ -115,11 +120,11 @@ and (2) enqueue the above kernel with appropriate grid/block sizes.
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.. GENERATED FROM PYTHON SOURCE LINES 75-76
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.. GENERATED FROM PYTHON SOURCE LINES 80-81
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We can now use the above function to compute the element-wise sum of two `torch.tensor` objects and test its correctness:
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.. GENERATED FROM PYTHON SOURCE LINES 76-90
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.. GENERATED FROM PYTHON SOURCE LINES 81-95
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.. code-block:: default
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@@ -154,11 +159,11 @@ We can now use the above function to compute the element-wise sum of two `torch.
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.. GENERATED FROM PYTHON SOURCE LINES 91-92
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.. GENERATED FROM PYTHON SOURCE LINES 96-97
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Seems like we're good to go!
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.. GENERATED FROM PYTHON SOURCE LINES 94-99
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.. GENERATED FROM PYTHON SOURCE LINES 99-104
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Benchmark
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-----------
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@@ -166,7 +171,7 @@ We can now benchmark our custom op on vectors of increasing sizes to get a sense
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To make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of your custom ops
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for different problem sizes.
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.. GENERATED FROM PYTHON SOURCE LINES 99-128
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.. GENERATED FROM PYTHON SOURCE LINES 104-133
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.. code-block:: default
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@@ -206,12 +211,12 @@ for different problem sizes.
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.. GENERATED FROM PYTHON SOURCE LINES 129-131
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.. GENERATED FROM PYTHON SOURCE LINES 134-136
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We can now run the decorated function above. Pass `print_data=True` to see the performance number, `show_plots=True` to plot them, and/or
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`save_path='/path/to/results/' to save them to disk along with raw CSV data
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.. GENERATED FROM PYTHON SOURCE LINES 131-132
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.. GENERATED FROM PYTHON SOURCE LINES 136-137
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.. code-block:: default
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@@ -232,22 +237,22 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
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vector-add-performance:
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size Triton Torch
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0 4096.0 9.600000 9.600000
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1 8192.0 19.200000 19.200000
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2 16384.0 38.400001 38.400001
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3 32768.0 76.800002 76.800002
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4 65536.0 127.999995 127.999995
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5 131072.0 219.428568 219.428568
|
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6 262144.0 341.333321 341.333321
|
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7 524288.0 472.615390 472.615390
|
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8 1048576.0 614.400016 614.400016
|
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9 2097152.0 722.823517 722.823517
|
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10 4194304.0 780.190482 780.190482
|
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11 8388608.0 812.429770 812.429770
|
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12 16777216.0 833.084721 833.084721
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13 33554432.0 842.004273 843.811163
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14 67108864.0 847.448255 848.362445
|
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15 134217728.0 849.737435 850.656574
|
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0 4096.0 4.800000 9.600000
|
||||
1 8192.0 8.727273 19.200000
|
||||
2 16384.0 17.454545 38.400001
|
||||
3 32768.0 38.400001 76.800002
|
||||
4 65536.0 69.818181 127.999995
|
||||
5 131072.0 139.636363 219.428568
|
||||
6 262144.0 219.428568 341.333321
|
||||
7 524288.0 341.333321 472.615390
|
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8 1048576.0 472.615390 614.400016
|
||||
9 2097152.0 614.400016 702.171410
|
||||
10 4194304.0 712.347810 780.190482
|
||||
11 8388608.0 774.047204 812.429770
|
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12 16777216.0 809.086412 833.084721
|
||||
13 33554432.0 829.569620 842.004273
|
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14 67108864.0 840.205105 848.362445
|
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15 134217728.0 845.625825 850.656574
|
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|
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|
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@@ -255,7 +260,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 1 minutes 42.600 seconds)
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**Total running time of the script:** ( 1 minutes 42.917 seconds)
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.. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:
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@@ -278,17 +278,17 @@ We will then compare its performance against (1) :code:`torch.softmax` and (2) t
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softmax-performance:
|
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N Triton Torch (native) Torch (jit)
|
||||
0 256.0 512.000001 546.133347 186.181817
|
||||
1 384.0 614.400016 585.142862 153.600004
|
||||
2 512.0 655.360017 606.814814 154.566038
|
||||
3 640.0 706.206879 640.000002 158.759699
|
||||
4 768.0 722.823517 664.216187 162.754967
|
||||
0 256.0 512.000001 546.133347 190.511628
|
||||
1 384.0 585.142862 585.142862 151.703707
|
||||
2 512.0 655.360017 585.142849 154.566038
|
||||
3 640.0 682.666684 640.000002 160.000000
|
||||
4 768.0 722.823517 646.736871 163.839992
|
||||
.. ... ... ... ...
|
||||
93 12160.0 814.058574 406.179533 198.834951
|
||||
94 12288.0 814.111783 415.661740 199.096718
|
||||
95 12416.0 814.163950 412.149375 198.655991
|
||||
96 12544.0 814.214963 412.971190 198.913776
|
||||
97 12672.0 814.265046 411.679167 198.971549
|
||||
93 12160.0 814.058574 405.755985 198.834951
|
||||
94 12288.0 814.111783 415.222812 199.197579
|
||||
95 12416.0 814.163950 412.149375 198.854847
|
||||
96 12544.0 814.214963 412.971190 199.012395
|
||||
97 12672.0 814.265046 412.097543 199.167004
|
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[98 rows x 4 columns]
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@@ -306,7 +306,7 @@ In the above plot, we can see that:
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 3 minutes 19.345 seconds)
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**Total running time of the script:** ( 3 minutes 27.571 seconds)
|
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.. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py:
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@@ -459,36 +459,36 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
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matmul-performance:
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M cuBLAS ... Triton Triton (+ LeakyReLU)
|
||||
0 256.0 2.730667 ... 3.276800 2.978909
|
||||
1 384.0 7.372800 ... 8.507077 7.899428
|
||||
2 512.0 14.563555 ... 16.384000 16.384000
|
||||
1 384.0 7.372800 ... 7.899428 8.507077
|
||||
2 512.0 14.563555 ... 15.420235 15.420235
|
||||
3 640.0 22.260869 ... 24.380953 24.380953
|
||||
4 768.0 32.768000 ... 34.028308 34.028308
|
||||
5 896.0 39.025776 ... 40.140799 39.025776
|
||||
6 1024.0 49.932191 ... 52.428801 52.428801
|
||||
7 1152.0 45.242181 ... 46.656000 46.656000
|
||||
8 1280.0 51.200001 ... 56.888887 56.888887
|
||||
9 1408.0 64.138541 ... 67.305878 66.485074
|
||||
10 1536.0 80.430545 ... 79.526831 78.643199
|
||||
11 1664.0 62.929456 ... 62.492442 61.636381
|
||||
12 1792.0 72.512412 ... 72.512412 72.047592
|
||||
13 1920.0 69.467336 ... 70.172588 69.818184
|
||||
14 2048.0 73.262953 ... 76.608294 76.608294
|
||||
15 2176.0 83.500614 ... 85.998493 85.632545
|
||||
16 2304.0 68.643310 ... 77.057651 76.319081
|
||||
17 2432.0 71.305746 ... 85.393507 85.134737
|
||||
18 2560.0 78.019048 ... 80.709358 81.108913
|
||||
19 2688.0 83.004501 ... 89.464755 89.254248
|
||||
20 2816.0 80.767055 ... 83.552120 82.602666
|
||||
21 2944.0 81.298583 ... 82.237674 81.967162
|
||||
22 3072.0 81.707223 ... 88.473602 87.516392
|
||||
23 3200.0 84.377059 ... 94.955488 94.674553
|
||||
24 3328.0 83.034941 ... 84.695641 83.905938
|
||||
25 3456.0 81.108217 ... 85.133652 81.029251
|
||||
26 3584.0 86.623693 ... 98.483450 98.160909
|
||||
27 3712.0 81.682211 ... 88.404730 84.017953
|
||||
28 3840.0 82.592983 ... 92.006659 85.169042
|
||||
29 3968.0 90.791620 ... 86.236000 90.522206
|
||||
30 4096.0 86.369197 ... 85.001726 91.304576
|
||||
4 768.0 32.768000 ... 35.389441 34.028308
|
||||
5 896.0 37.971025 ... 40.140799 39.025776
|
||||
6 1024.0 49.932191 ... 53.773130 53.773130
|
||||
7 1152.0 45.242181 ... 48.161033 47.396572
|
||||
8 1280.0 51.200001 ... 57.690139 57.690139
|
||||
9 1408.0 64.138541 ... 69.009825 67.305878
|
||||
10 1536.0 79.526831 ... 80.430545 79.526831
|
||||
11 1664.0 63.372618 ... 63.372618 62.929456
|
||||
12 1792.0 72.983276 ... 63.142831 63.142831
|
||||
13 1920.0 69.120002 ... 71.626943 70.892307
|
||||
14 2048.0 73.584279 ... 78.033565 77.672296
|
||||
15 2176.0 83.500614 ... 87.115360 86.739860
|
||||
16 2304.0 68.446623 ... 77.810656 77.307030
|
||||
17 2432.0 71.125224 ... 75.522751 75.320281
|
||||
18 2560.0 77.833728 ... 81.715711 81.512437
|
||||
19 2688.0 83.737433 ... 90.966561 90.532356
|
||||
20 2816.0 79.443003 ... 82.916747 84.035084
|
||||
21 2944.0 81.832567 ... 83.198715 82.102191
|
||||
22 3072.0 81.121923 ... 88.197981 87.381335
|
||||
23 3200.0 83.116885 ... 96.385543 96.096095
|
||||
24 3328.0 82.939284 ... 84.895397 85.096096
|
||||
25 3456.0 77.745004 ... 86.596744 84.332184
|
||||
26 3584.0 86.540320 ... 91.380335 97.416461
|
||||
27 3712.0 85.748791 ... 88.404730 86.341700
|
||||
28 3840.0 84.874902 ... 93.247896 85.300426
|
||||
29 3968.0 92.372393 ... 80.015697 78.220472
|
||||
30 4096.0 93.990003 ... 93.206754 86.036145
|
||||
|
||||
[31 rows x 5 columns]
|
||||
|
||||
@@ -498,7 +498,7 @@ We can now compare the performance of our kernel against that of cuBLAS. Here we
|
||||
|
||||
.. rst-class:: sphx-glr-timing
|
||||
|
||||
**Total running time of the script:** ( 6 minutes 0.645 seconds)
|
||||
**Total running time of the script:** ( 6 minutes 48.521 seconds)
|
||||
|
||||
|
||||
.. _sphx_glr_download_getting-started_tutorials_03-matrix-multiplication.py:
|
||||
|
||||
@@ -240,7 +240,7 @@ References
|
||||
|
||||
.. rst-class:: sphx-glr-timing
|
||||
|
||||
**Total running time of the script:** ( 0 minutes 0.482 seconds)
|
||||
**Total running time of the script:** ( 0 minutes 0.327 seconds)
|
||||
|
||||
|
||||
.. _sphx_glr_download_getting-started_tutorials_04-low-memory-dropout.py:
|
||||
|
||||
@@ -38,36 +38,36 @@ Layer Normalization
|
||||
|
||||
layer-norm-backward:
|
||||
N Triton Torch Apex
|
||||
0 1024.0 311.088617 98.303995 303.407414
|
||||
1 1536.0 347.773587 134.050910 341.333333
|
||||
2 2048.0 420.102553 161.684218 325.509933
|
||||
3 2560.0 455.111129 181.238943 326.808501
|
||||
4 3072.0 511.999982 191.999993 317.793096
|
||||
5 3584.0 547.872604 207.768111 310.527060
|
||||
6 4096.0 564.965515 219.919464 295.207204
|
||||
7 4608.0 504.986315 232.825259 291.799469
|
||||
8 5120.0 527.381977 242.845844 288.450695
|
||||
9 5632.0 542.843364 243.107920 288.820505
|
||||
10 6144.0 546.133354 248.661056 286.879370
|
||||
11 6656.0 532.479975 256.000009 285.767438
|
||||
12 7168.0 505.976473 260.654538 286.242939
|
||||
13 7680.0 481.253256 262.190612 278.429013
|
||||
14 8192.0 463.698115 267.130429 284.939124
|
||||
15 8704.0 417.791980 267.815384 284.987724
|
||||
16 9216.0 432.845409 272.394084 288.751954
|
||||
17 9728.0 439.683593 280.278512 289.667485
|
||||
18 10240.0 448.467168 286.433562 290.153487
|
||||
19 10752.0 425.821771 247.172406 290.922209
|
||||
20 11264.0 427.071098 245.760001 286.676558
|
||||
21 11776.0 423.724129 249.667843 288.981596
|
||||
22 12288.0 419.504980 254.673582 294.323369
|
||||
23 12800.0 413.458944 253.674644 288.180121
|
||||
24 13312.0 412.242569 252.859526 289.916513
|
||||
25 13824.0 405.594132 257.190689 292.056329
|
||||
26 14336.0 394.568805 254.297107 286.719986
|
||||
27 14848.0 386.498925 257.665934 289.246765
|
||||
28 15360.0 373.117425 257.970599 286.211174
|
||||
29 15872.0 371.274849 261.806182 289.899545
|
||||
0 1024.0 356.173905 99.497980 315.076934
|
||||
1 1536.0 405.098894 134.050910 344.523365
|
||||
2 2048.0 486.653476 159.067963 321.254900
|
||||
3 2560.0 458.507457 182.314537 326.808501
|
||||
4 3072.0 515.580429 191.501303 319.168834
|
||||
5 3584.0 551.384634 207.768111 307.199992
|
||||
6 4096.0 568.231237 220.907859 293.444785
|
||||
7 4608.0 502.690905 232.336141 290.267724
|
||||
8 5120.0 527.381977 243.326731 287.102804
|
||||
9 5632.0 540.671974 244.426754 291.310338
|
||||
10 6144.0 548.163546 251.202731 288.000001
|
||||
11 6656.0 532.479975 255.590406 286.279570
|
||||
12 7168.0 510.480705 253.734520 277.919225
|
||||
13 7680.0 487.619051 266.743841 284.884090
|
||||
14 8192.0 468.114289 258.694729 278.481578
|
||||
15 8704.0 415.300208 267.472468 284.987724
|
||||
16 9216.0 429.483477 272.394084 290.077383
|
||||
17 9728.0 438.033784 280.278512 288.950501
|
||||
18 10240.0 443.610086 287.102804 290.153487
|
||||
19 10752.0 426.525614 246.699797 290.267711
|
||||
20 11264.0 427.071098 245.536784 286.069848
|
||||
21 11776.0 418.702211 249.447482 288.981596
|
||||
22 12288.0 414.784810 254.673582 294.323369
|
||||
23 12800.0 410.146863 254.094291 288.180121
|
||||
24 13312.0 409.599999 252.161013 289.129403
|
||||
25 13824.0 404.112047 257.190689 291.799461
|
||||
26 14336.0 395.930964 256.000002 289.129416
|
||||
27 14848.0 385.662341 257.479779 288.777966
|
||||
28 15360.0 380.433442 258.332158 286.656296
|
||||
29 15872.0 372.363640 261.806182 290.562936
|
||||
|
||||
|
||||
|
||||
@@ -339,7 +339,7 @@ Layer Normalization
|
||||
|
||||
.. rst-class:: sphx-glr-timing
|
||||
|
||||
**Total running time of the script:** ( 2 minutes 12.550 seconds)
|
||||
**Total running time of the script:** ( 2 minutes 14.583 seconds)
|
||||
|
||||
|
||||
.. _sphx_glr_download_getting-started_tutorials_05-layer-norm.py:
|
||||
|
||||
@@ -5,16 +5,16 @@
|
||||
|
||||
Computation times
|
||||
=================
|
||||
**13:15.622** total execution time for **getting-started_tutorials** files:
|
||||
**14:13.919** total execution time for **getting-started_tutorials** files:
|
||||
|
||||
+---------------------------------------------------------------------------------------------------------+-----------+--------+
|
||||
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:00.645 | 0.0 MB |
|
||||
| :ref:`sphx_glr_getting-started_tutorials_03-matrix-multiplication.py` (``03-matrix-multiplication.py``) | 06:48.521 | 0.0 MB |
|
||||
+---------------------------------------------------------------------------------------------------------+-----------+--------+
|
||||
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 03:19.345 | 0.0 MB |
|
||||
| :ref:`sphx_glr_getting-started_tutorials_02-fused-softmax.py` (``02-fused-softmax.py``) | 03:27.571 | 0.0 MB |
|
||||
+---------------------------------------------------------------------------------------------------------+-----------+--------+
|
||||
| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``) | 02:12.550 | 0.0 MB |
|
||||
| :ref:`sphx_glr_getting-started_tutorials_05-layer-norm.py` (``05-layer-norm.py``) | 02:14.583 | 0.0 MB |
|
||||
+---------------------------------------------------------------------------------------------------------+-----------+--------+
|
||||
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 01:42.600 | 0.0 MB |
|
||||
| :ref:`sphx_glr_getting-started_tutorials_01-vector-add.py` (``01-vector-add.py``) | 01:42.917 | 0.0 MB |
|
||||
+---------------------------------------------------------------------------------------------------------+-----------+--------+
|
||||
| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``) | 00:00.482 | 0.0 MB |
|
||||
| :ref:`sphx_glr_getting-started_tutorials_04-low-memory-dropout.py` (``04-low-memory-dropout.py``) | 00:00.327 | 0.0 MB |
|
||||
+---------------------------------------------------------------------------------------------------------+-----------+--------+
|
||||
|
||||
@@ -214,9 +214,11 @@ to download the full example code</p>
|
||||
<span class="n">y_ptr</span><span class="p">,</span> <span class="c1"># *Pointer* to second input vector</span>
|
||||
<span class="n">output_ptr</span><span class="p">,</span> <span class="c1"># *Pointer* to output vector</span>
|
||||
<span class="n">n_elements</span><span class="p">,</span> <span class="c1"># Size of the vector</span>
|
||||
<span class="n">time_start_ptr</span><span class="p">,</span> <span class="n">time_end_ptr</span><span class="p">,</span>
|
||||
<span class="n">BLOCK_SIZE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="c1"># Number of elements each program should process</span>
|
||||
<span class="c1"># NOTE: `constexpr` so it can be used as a shape value</span>
|
||||
<span class="p">):</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">atomic_min</span><span class="p">(</span><span class="n">time_start_ptr</span><span class="p">,</span> <span class="n">tl</span><span class="o">.</span><span class="n">clock</span><span class="p">())</span>
|
||||
<span class="c1"># There are multiple 'program's processing different data. We identify which program</span>
|
||||
<span class="c1"># we are here</span>
|
||||
<span class="n">pid</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># We use a 1D launch grid so axis is 0</span>
|
||||
@@ -235,11 +237,14 @@ to download the full example code</p>
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
|
||||
<span class="c1"># Write x + y back to DRAM</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">output_ptr</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">)</span>
|
||||
<span class="n">tl</span><span class="o">.</span><span class="n">atomic_max</span><span class="p">(</span><span class="n">time_end_ptr</span><span class="p">,</span> <span class="n">tl</span><span class="o">.</span><span class="n">clock</span><span class="p">())</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Let’s also declare a helper function to (1) allocate the <cite>z</cite> tensor
|
||||
and (2) enqueue the above kernel with appropriate grid/block sizes.</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
|
||||
<span class="n">time_start</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
<span class="n">time_end</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
<span class="c1"># We need to preallocate the output</span>
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">x</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">y</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">output</span><span class="o">.</span><span class="n">is_cuda</span>
|
||||
@@ -252,7 +257,7 @@ and (2) enqueue the above kernel with appropriate grid/block sizes.</p>
|
||||
<span class="c1"># - each torch.tensor object is implicitly converted into a pointer to its first element.</span>
|
||||
<span class="c1"># - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel</span>
|
||||
<span class="c1"># - don't forget to pass meta-parameters as keywords arguments</span>
|
||||
<span class="n">add_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">n_elements</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
|
||||
<span class="n">add_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">n_elements</span><span class="p">,</span> <span class="n">time_start</span><span class="p">,</span> <span class="n">time_end</span><span class="p">,</span> <span class="n">BLOCK_SIZE</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
|
||||
<span class="c1"># We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still</span>
|
||||
<span class="c1"># running asynchronously at this point.</span>
|
||||
<span class="k">return</span> <span class="n">output</span>
|
||||
@@ -322,25 +327,25 @@ for different problem sizes.</p>
|
||||
<p class="sphx-glr-script-out">Out:</p>
|
||||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>vector-add-performance:
|
||||
size Triton Torch
|
||||
0 4096.0 9.600000 9.600000
|
||||
1 8192.0 19.200000 19.200000
|
||||
2 16384.0 38.400001 38.400001
|
||||
3 32768.0 76.800002 76.800002
|
||||
4 65536.0 127.999995 127.999995
|
||||
5 131072.0 219.428568 219.428568
|
||||
6 262144.0 341.333321 341.333321
|
||||
7 524288.0 472.615390 472.615390
|
||||
8 1048576.0 614.400016 614.400016
|
||||
9 2097152.0 722.823517 722.823517
|
||||
10 4194304.0 780.190482 780.190482
|
||||
11 8388608.0 812.429770 812.429770
|
||||
12 16777216.0 833.084721 833.084721
|
||||
13 33554432.0 842.004273 843.811163
|
||||
14 67108864.0 847.448255 848.362445
|
||||
15 134217728.0 849.737435 850.656574
|
||||
0 4096.0 4.800000 9.600000
|
||||
1 8192.0 8.727273 19.200000
|
||||
2 16384.0 17.454545 38.400001
|
||||
3 32768.0 38.400001 76.800002
|
||||
4 65536.0 69.818181 127.999995
|
||||
5 131072.0 139.636363 219.428568
|
||||
6 262144.0 219.428568 341.333321
|
||||
7 524288.0 341.333321 472.615390
|
||||
8 1048576.0 472.615390 614.400016
|
||||
9 2097152.0 614.400016 702.171410
|
||||
10 4194304.0 712.347810 780.190482
|
||||
11 8388608.0 774.047204 812.429770
|
||||
12 16777216.0 809.086412 833.084721
|
||||
13 33554432.0 829.569620 842.004273
|
||||
14 67108864.0 840.205105 848.362445
|
||||
15 134217728.0 845.625825 850.656574
|
||||
</pre></div>
|
||||
</div>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 42.600 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 42.917 seconds)</p>
|
||||
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-01-vector-add-py">
|
||||
<div class="sphx-glr-download sphx-glr-download-python docutils container">
|
||||
<p><a class="reference download internal" download="" href="../../_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">01-vector-add.py</span></code></a></p>
|
||||
|
||||
@@ -369,17 +369,17 @@ We will then compare its performance against (1) <code class="code docutils lite
|
||||
<p class="sphx-glr-script-out">Out:</p>
|
||||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>softmax-performance:
|
||||
N Triton Torch (native) Torch (jit)
|
||||
0 256.0 512.000001 546.133347 186.181817
|
||||
1 384.0 614.400016 585.142862 153.600004
|
||||
2 512.0 655.360017 606.814814 154.566038
|
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|
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|
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.. ... ... ... ...
|
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|
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[98 rows x 4 columns]
|
||||
</pre></div>
|
||||
@@ -392,7 +392,7 @@ We will then compare its performance against (1) <code class="code docutils lite
|
||||
Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
|
||||
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|
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 19.345 seconds)</p>
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
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<p><a class="reference download internal" download="" href="../../_downloads/d91442ac2982c4e0cc3ab0f43534afbc/02-fused-softmax.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">02-fused-softmax.py</span></code></a></p>
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||||
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@@ -565,41 +565,41 @@ torch_output=tensor([[ 1.1045, -36.9688, 31.4688, ..., -11.3906, 24.4531, -3
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>matmul-performance:
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||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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26 3584.0 86.623693 ... 98.483450 98.160909
|
||||
27 3712.0 81.682211 ... 88.404730 84.017953
|
||||
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|
||||
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|
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||||
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|
||||
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||||
11 1664.0 63.372618 ... 63.372618 62.929456
|
||||
12 1792.0 72.983276 ... 63.142831 63.142831
|
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||||
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|
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||||
16 2304.0 68.446623 ... 77.810656 77.307030
|
||||
17 2432.0 71.125224 ... 75.522751 75.320281
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||||
18 2560.0 77.833728 ... 81.715711 81.512437
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19 2688.0 83.737433 ... 90.966561 90.532356
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20 2816.0 79.443003 ... 82.916747 84.035084
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|
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29 3968.0 92.372393 ... 80.015697 78.220472
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30 4096.0 93.990003 ... 93.206754 86.036145
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes 0.645 seconds)</p>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes 48.521 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-03-matrix-multiplication-py">
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
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<p><a class="reference download internal" download="" href="../../_downloads/d5fee5b55a64e47f1b5724ec39adf171/03-matrix-multiplication.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">03-matrix-multiplication.py</span></code></a></p>
|
||||
|
||||
@@ -372,7 +372,7 @@ to explore the <cite>triton/language/random</cite> folder!</p>
|
||||
<dd><p>Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014</p>
|
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</dd>
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<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.482 seconds)</p>
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<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-04-low-memory-dropout-py">
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<div class="sphx-glr-download sphx-glr-download-python docutils container">
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<p><a class="reference download internal" download="" href="../../_downloads/c9aed78977a4c05741d675a38dde3d7d/04-low-memory-dropout.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">04-low-memory-dropout.py</span></code></a></p>
|
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|
||||
@@ -194,36 +194,36 @@ to download the full example code</p>
|
||||
<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>layer-norm-backward:
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||||
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|
||||
29 15872.0 372.363640 261.806182 290.562936
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="line-block">
|
||||
@@ -487,7 +487,7 @@ to download the full example code</p>
|
||||
<span class="n">bench_layer_norm</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">save_path</span><span class="o">=</span><span class="s1">'.'</span><span class="p">,</span> <span class="n">print_data</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 12.550 seconds)</p>
|
||||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes 14.583 seconds)</p>
|
||||
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-getting-started-tutorials-05-layer-norm-py">
|
||||
<div class="sphx-glr-download sphx-glr-download-python docutils container">
|
||||
<p><a class="reference download internal" download="" href="../../_downloads/935c0dd0fbeb4b2e69588471cbb2d4b2/05-layer-norm.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">05-layer-norm.py</span></code></a></p>
|
||||
|
||||
@@ -174,7 +174,7 @@
|
||||
|
||||
<div class="section" id="computation-times">
|
||||
<span id="sphx-glr-getting-started-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
|
||||
<p><strong>13:15.622</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<p><strong>14:13.919</strong> total execution time for <strong>getting-started_tutorials</strong> files:</p>
|
||||
<table class="docutils align-default">
|
||||
<colgroup>
|
||||
<col style="width: 85%" />
|
||||
@@ -183,23 +183,23 @@
|
||||
</colgroup>
|
||||
<tbody>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="03-matrix-multiplication.html#sphx-glr-getting-started-tutorials-03-matrix-multiplication-py"><span class="std std-ref">Matrix Multiplication</span></a> (<code class="docutils literal notranslate"><span class="pre">03-matrix-multiplication.py</span></code>)</p></td>
|
||||
<td><p>06:00.645</p></td>
|
||||
<td><p>06:48.521</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="02-fused-softmax.html#sphx-glr-getting-started-tutorials-02-fused-softmax-py"><span class="std std-ref">Fused Softmax</span></a> (<code class="docutils literal notranslate"><span class="pre">02-fused-softmax.py</span></code>)</p></td>
|
||||
<td><p>03:19.345</p></td>
|
||||
<td><p>03:27.571</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="05-layer-norm.html#sphx-glr-getting-started-tutorials-05-layer-norm-py"><span class="std std-ref">Layer Normalization</span></a> (<code class="docutils literal notranslate"><span class="pre">05-layer-norm.py</span></code>)</p></td>
|
||||
<td><p>02:12.550</p></td>
|
||||
<td><p>02:14.583</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="01-vector-add.html#sphx-glr-getting-started-tutorials-01-vector-add-py"><span class="std std-ref">Vector Addition</span></a> (<code class="docutils literal notranslate"><span class="pre">01-vector-add.py</span></code>)</p></td>
|
||||
<td><p>01:42.600</p></td>
|
||||
<td><p>01:42.917</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="04-low-memory-dropout.html#sphx-glr-getting-started-tutorials-04-low-memory-dropout-py"><span class="std std-ref">Low-Memory Dropout</span></a> (<code class="docutils literal notranslate"><span class="pre">04-low-memory-dropout.py</span></code>)</p></td>
|
||||
<td><p>00:00.482</p></td>
|
||||
<td><p>00:00.327</p></td>
|
||||
<td><p>0.0 MB</p></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
|
||||
@@ -200,11 +200,11 @@
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="triton.language.broadcast_to">
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">broadcast_to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.broadcast_to" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Tries to broadcast the given block to a new <code class="code docutils literal notranslate"><span class="pre">shape</span></code>.</p>
|
||||
<dd><p>Tries to broadcast the given tensor to a new <code class="code docutils literal notranslate"><span class="pre">shape</span></code>.</p>
|
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<dl class="field-list simple">
|
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<dt class="field-odd">Parameters</dt>
|
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|
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<li><p><strong>input</strong> (<em>Block</em>) – The input block.</p></li>
|
||||
<li><p><strong>input</strong> (<em>Block</em>) – The input tensor.</p></li>
|
||||
<li><p><strong>shape</strong> (<em>Tuple</em><em>[</em><em>int</em><em>]</em>) – The desired shape.</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
|
||||
@@ -203,8 +203,8 @@
|
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<dl class="field-list simple">
|
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<dt class="field-odd">Parameters</dt>
|
||||
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<li><p><strong>input</strong> (2D block of scalar-type in {<code class="code docutils literal notranslate"><span class="pre">float16</span></code>, <code class="code docutils literal notranslate"><span class="pre">bfloat16</span></code>, <code class="code docutils literal notranslate"><span class="pre">float32</span></code>}) – The first block to be multiplied.</p></li>
|
||||
<li><p><strong>other</strong> (2D block of scalar-type in {<code class="code docutils literal notranslate"><span class="pre">float16</span></code>, <code class="code docutils literal notranslate"><span class="pre">bfloat16</span></code>, <code class="code docutils literal notranslate"><span class="pre">float32</span></code>}) – The second block to be multiplied.</p></li>
|
||||
<li><p><strong>input</strong> (2D tensor of scalar-type in {<code class="code docutils literal notranslate"><span class="pre">float16</span></code>, <code class="code docutils literal notranslate"><span class="pre">bfloat16</span></code>, <code class="code docutils literal notranslate"><span class="pre">float32</span></code>}) – The first tensor to be multiplied.</p></li>
|
||||
<li><p><strong>other</strong> (2D tensor of scalar-type in {<code class="code docutils literal notranslate"><span class="pre">float16</span></code>, <code class="code docutils literal notranslate"><span class="pre">bfloat16</span></code>, <code class="code docutils literal notranslate"><span class="pre">float32</span></code>}) – The second tensor to be multiplied.</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
|
||||
@@ -201,7 +201,7 @@
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="triton.language.load">
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_modifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eviction_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">volatile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.load" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Return a block of data whose values are, elementwise, loaded from memory at location defined by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
|
||||
<dd><p>Return a tensor of data whose values are, elementwise, loaded from memory at location defined by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
|
||||
<p><code class="code docutils literal notranslate"><span class="pre">mask</span></code> and <code class="code docutils literal notranslate"><span class="pre">other</span></code> are implicitly broadcast to <code class="code docutils literal notranslate"><span class="pre">pointer.shape</span></code>.</p>
|
||||
<p><code class="code docutils literal notranslate"><span class="pre">other</span></code> is implicitly typecast to <code class="code docutils literal notranslate"><span class="pre">pointer.dtype.element_ty</span></code>.</p>
|
||||
<dl class="field-list simple">
|
||||
|
||||
@@ -200,7 +200,7 @@
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="triton.language.max">
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.max" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Returns the maximum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> block along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p>
|
||||
<dd><p>Returns the maximum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
|
||||
@@ -203,8 +203,8 @@
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>input</strong> (<em>Block</em>) – the first input block</p></li>
|
||||
<li><p><strong>other</strong> (<em>Block</em>) – the second input block</p></li>
|
||||
<li><p><strong>input</strong> (<em>Block</em>) – the first input tensor</p></li>
|
||||
<li><p><strong>other</strong> (<em>Block</em>) – the second input tensor</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
|
||||
@@ -200,7 +200,7 @@
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="triton.language.min">
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.min" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Returns the minimum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> block along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p>
|
||||
<dd><p>Returns the minimum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
|
||||
@@ -203,8 +203,8 @@
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>input</strong> (<em>Block</em>) – the first input block</p></li>
|
||||
<li><p><strong>other</strong> (<em>Block</em>) – the second input block</p></li>
|
||||
<li><p><strong>input</strong> (<em>Block</em>) – the first input tensor</p></li>
|
||||
<li><p><strong>other</strong> (<em>Block</em>) – the second input tensor</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
|
||||
@@ -203,7 +203,7 @@
|
||||
<dd><p>Returns a contiguous flattened view of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>x</strong> (<em>Block</em>) – the input block</p>
|
||||
<dd class="field-odd"><p><strong>x</strong> (<em>Block</em>) – the input tensor</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
@@ -200,11 +200,11 @@
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="triton.language.reshape">
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">reshape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.reshape" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Tries to reshape the given block to a new shape.</p>
|
||||
<dd><p>Tries to reshape the given tensor to a new shape.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>input</strong> – The input block.</p></li>
|
||||
<li><p><strong>input</strong> – The input tensor.</p></li>
|
||||
<li><p><strong>shape</strong> (<em>Tuple</em><em>[</em><em>int</em><em>]</em>) – The desired shape.</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
|
||||
@@ -203,7 +203,7 @@
|
||||
<h1>triton.language.softmax<a class="headerlink" href="#triton-language-softmax" title="Permalink to this headline">¶</a></h1>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="triton.language.softmax">
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ieee_rounding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.softmax" title="Permalink to this definition">¶</a></dt>
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ieee_rounding</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">triton.language.core.constexpr</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.softmax" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Computes the element-wise softmax of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
|
||||
@@ -201,13 +201,13 @@
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="triton.language.store">
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">store</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pointer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.store" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Stores <code class="code docutils literal notranslate"><span class="pre">value</span></code> block of elements in memory, element-wise, at the memory locations specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
|
||||
<dd><p>Stores <code class="code docutils literal notranslate"><span class="pre">value</span></code> tensor of elements in memory, element-wise, at the memory locations specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p>
|
||||
<p><code class="code docutils literal notranslate"><span class="pre">value</span></code> is implicitly broadcast to <code class="code docutils literal notranslate"><span class="pre">pointer.shape</span></code> and typecast to <code class="code docutils literal notranslate"><span class="pre">pointer.dtype.element_ty</span></code>.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>pointer</strong> (<em>Block of dtype=triton.PointerDType</em>) – The memory locations where the elements of <code class="code docutils literal notranslate"><span class="pre">value</span></code> are stored.</p></li>
|
||||
<li><p><strong>value</strong> (<em>Block</em>) – The block of elements to be stored.</p></li>
|
||||
<li><p><strong>value</strong> (<em>Block</em>) – The tensor of elements to be stored.</p></li>
|
||||
<li><p><strong>mask</strong> (<em>Block of triton.int1</em><em>, </em><em>optional</em>) – If mask[idx] is false, do not store <code class="code docutils literal notranslate"><span class="pre">value[idx]</span></code> at <code class="code docutils literal notranslate"><span class="pre">pointer[idx]</span></code>.</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
|
||||
@@ -200,7 +200,7 @@
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="triton.language.sum">
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.sum" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Returns the sum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> block along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p>
|
||||
<dd><p>Returns the sum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
|
||||
@@ -198,7 +198,7 @@
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="triton.language.where">
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">where</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">condition</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.where" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Returns a block of elements from either <code class="code docutils literal notranslate"><span class="pre">x</span></code> or <code class="code docutils literal notranslate"><span class="pre">y</span></code>, depending on <code class="code docutils literal notranslate"><span class="pre">condition</span></code>.</p>
|
||||
<dd><p>Returns a tensor of elements from either <code class="code docutils literal notranslate"><span class="pre">x</span></code> or <code class="code docutils literal notranslate"><span class="pre">y</span></code>, depending on <code class="code docutils literal notranslate"><span class="pre">condition</span></code>.</p>
|
||||
<p>Note that <code class="code docutils literal notranslate"><span class="pre">x</span></code> and <code class="code docutils literal notranslate"><span class="pre">y</span></code> are always evaluated regardless of the value of <code class="code docutils literal notranslate"><span class="pre">condition</span></code>.</p>
|
||||
<p>If you want to avoid unintented memory operations, use the <code class="code docutils literal notranslate"><span class="pre">mask</span></code> arguments in <cite>triton.load</cite> and <cite>triton.store</cite> instead.</p>
|
||||
<p>The shape of <code class="code docutils literal notranslate"><span class="pre">x</span></code> and <code class="code docutils literal notranslate"><span class="pre">y</span></code> are both broadcast to the shape of <code class="code docutils literal notranslate"><span class="pre">condition</span></code>.
|
||||
|
||||
@@ -199,7 +199,7 @@
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="triton.language.zeros">
|
||||
<span class="sig-prename descclassname"><span class="pre">triton.language.</span></span><span class="sig-name descname"><span class="pre">zeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.language.zeros" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Returns a block filled with the scalar value 0 for the given <code class="code docutils literal notranslate"><span class="pre">shape</span></code> and <code class="code docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
|
||||
<dd><p>Returns a tensor filled with the scalar value 0 for the given <code class="code docutils literal notranslate"><span class="pre">shape</span></code> and <code class="code docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
|
||||
@@ -278,7 +278,7 @@
|
||||
<td><p>Returns contiguous values within the open interval [<code class="code docutils literal notranslate"><span class="pre">start</span></code>, <code class="code docutils literal notranslate"><span class="pre">end</span></code>).</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.zeros.html#triton.language.zeros" title="triton.language.zeros"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zeros</span></code></a></p></td>
|
||||
<td><p>Returns a block filled with the scalar value 0 for the given <code class="code docutils literal notranslate"><span class="pre">shape</span></code> and <code class="code docutils literal notranslate"><span class="pre">dtype</span></code>.</p></td>
|
||||
<td><p>Returns a tensor filled with the scalar value 0 for the given <code class="code docutils literal notranslate"><span class="pre">shape</span></code> and <code class="code docutils literal notranslate"><span class="pre">dtype</span></code>.</p></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
@@ -292,10 +292,10 @@
|
||||
</colgroup>
|
||||
<tbody>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.broadcast_to.html#triton.language.broadcast_to" title="triton.language.broadcast_to"><code class="xref py py-obj docutils literal notranslate"><span class="pre">broadcast_to</span></code></a></p></td>
|
||||
<td><p>Tries to broadcast the given block to a new <code class="code docutils literal notranslate"><span class="pre">shape</span></code>.</p></td>
|
||||
<td><p>Tries to broadcast the given tensor to a new <code class="code docutils literal notranslate"><span class="pre">shape</span></code>.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.reshape.html#triton.language.reshape" title="triton.language.reshape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reshape</span></code></a></p></td>
|
||||
<td><p>Tries to reshape the given block to a new shape.</p></td>
|
||||
<td><p>Tries to reshape the given tensor to a new shape.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.ravel.html#triton.language.ravel" title="triton.language.ravel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ravel</span></code></a></p></td>
|
||||
<td><p>Returns a contiguous flattened view of <code class="code docutils literal notranslate"><span class="pre">x</span></code></p></td>
|
||||
@@ -326,10 +326,10 @@
|
||||
</colgroup>
|
||||
<tbody>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.load.html#triton.language.load" title="triton.language.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a></p></td>
|
||||
<td><p>Return a block of data whose values are, elementwise, loaded from memory at location defined by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
|
||||
<td><p>Return a tensor of data whose values are, elementwise, loaded from memory at location defined by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.store.html#triton.language.store" title="triton.language.store"><code class="xref py py-obj docutils literal notranslate"><span class="pre">store</span></code></a></p></td>
|
||||
<td><p>Stores <code class="code docutils literal notranslate"><span class="pre">value</span></code> block of elements in memory, element-wise, at the memory locations specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
|
||||
<td><p>Stores <code class="code docutils literal notranslate"><span class="pre">value</span></code> tensor of elements in memory, element-wise, at the memory locations specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.atomic_cas.html#triton.language.atomic_cas" title="triton.language.atomic_cas"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atomic_cas</span></code></a></p></td>
|
||||
<td><p>Performs an atomic compare-and-swap at the memory location specified by <code class="code docutils literal notranslate"><span class="pre">pointer</span></code>.</p></td>
|
||||
@@ -349,7 +349,7 @@
|
||||
</colgroup>
|
||||
<tbody>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.where.html#triton.language.where" title="triton.language.where"><code class="xref py py-obj docutils literal notranslate"><span class="pre">where</span></code></a></p></td>
|
||||
<td><p>Returns a block of elements from either <code class="code docutils literal notranslate"><span class="pre">x</span></code> or <code class="code docutils literal notranslate"><span class="pre">y</span></code>, depending on <code class="code docutils literal notranslate"><span class="pre">condition</span></code>.</p></td>
|
||||
<td><p>Returns a tensor of elements from either <code class="code docutils literal notranslate"><span class="pre">x</span></code> or <code class="code docutils literal notranslate"><span class="pre">y</span></code>, depending on <code class="code docutils literal notranslate"><span class="pre">condition</span></code>.</p></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
@@ -395,13 +395,13 @@
|
||||
</colgroup>
|
||||
<tbody>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.max.html#triton.language.max" title="triton.language.max"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max</span></code></a></p></td>
|
||||
<td><p>Returns the maximum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> block along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p></td>
|
||||
<td><p>Returns the maximum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p><a class="reference internal" href="generated/triton.language.min.html#triton.language.min" title="triton.language.min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min</span></code></a></p></td>
|
||||
<td><p>Returns the minimum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> block along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p></td>
|
||||
<td><p>Returns the minimum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p><a class="reference internal" href="generated/triton.language.sum.html#triton.language.sum" title="triton.language.sum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sum</span></code></a></p></td>
|
||||
<td><p>Returns the sum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> block along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p></td>
|
||||
<td><p>Returns the sum of all elements in the <code class="code docutils literal notranslate"><span class="pre">input</span></code> tensor along the provided <code class="code docutils literal notranslate"><span class="pre">axis</span></code></p></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
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@@ -1,4 +1,4 @@
|
||||
# Sphinx build info version 1
|
||||
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
|
||||
config: 27fe8ebc933e4ad0f9352faba2dbdccd
|
||||
config: cad12a755c359cf8dbd8410700935254
|
||||
tags: 645f666f9bcd5a90fca523b33c5a78b7
|
||||
|
||||
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Reference in New Issue
Block a user